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One-class classification-based control charts for multivariate process monitoring

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  • Thuntee Sukchotrat
  • Seoung Kim
  • Fugee Tsung

Abstract

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.

Suggested Citation

  • Thuntee Sukchotrat & Seoung Kim & Fugee Tsung, 2010. "One-class classification-based control charts for multivariate process monitoring," IISE Transactions, Taylor & Francis Journals, vol. 42(2), pages 107-120.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:2:p:107-120
    DOI: 10.1080/07408170903019150
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    Cited by:

    1. Shuguang He & Wei Jiang & Houtao Deng, 2018. "A distance-based control chart for monitoring multivariate processes using support vector machines," Annals of Operations Research, Springer, vol. 263(1), pages 191-207, April.
    2. Roberto Campos Leoni & Marcela Aparecida Guerreiro Machado & Antonio Fernando Branco Costa, 2016. "The T -super-2 chart with mixed samples to control bivariate autocorrelated processes," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3294-3310, June.

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